Design of Fingerprint-Based Gender Clustering Using Fuzzy C-Means Algorithm

Abstract

Biometric systems, with fingerprint recognition as a cornerstone, play a crucial role in identity verification and access control. This paper introduces an innovative approach to gender clustering based on fingerprint data, leveraging the Fuzzy C-Means (FCM) algorithm. The proposed design aims to enhance the precision of gender classification by incorporating the inherent uncertainty in fingerprint features. Through the integration of FCM, which allows soft assignment of fingerprints to gender clusters, the system achieves a nuanced and adaptable classification. The study meticulously explores the design considerations, including the selection of features, data preprocessing, and the configuration of FCM parameters. Additionally, the paper discusses the experimental results, demonstrating the effectiveness of the proposed method in accurately clustering fingerprint data according to gender. The research contributes to the evolving field of biometrics, offering a novel perspective on gender classification that embraces the complexity of fingerprint patterns. The outcomes provide valuable insights for the development of reliable and versatile biometric systems, with potential applications in security, forensics, and personalized identification